摘要：

针对以多属性概率语言集为信息环境的人岗匹配决策问题, 构建基于改进ORESTE排序法和匹配意愿的双边匹配模型。提出概率语言广义兰氏距离公式, 利用概率语言幂平均算子确定属性客观权重, 并基于博弈论的思想对主、客观权重进行组合优化, 从而克服极端值对决策结果的影响, 并使得属性权重兼顾专家经验判断的主观分析和信息结构的客观分析两方面因素, 更具科学性。改进ORESTE排序法, 在ORESTE排序法的弱排序与强排序相结合的基础上, 通过引入概率语言广义兰氏距离公式和Borda函数, 同时考虑最优化组合的主客观权重, 从而使排序结果更加真实与符合实际。为了最大化地满足主体意愿, 根据心理行为“首因效应”, 提出具有稳定性的新匹配意愿系数, 以此构建合理有效的多目标双边匹配模型。在某智慧养老服务平台上的养老服务人岗匹配算例结果表明, 该双边匹配模型具有有效性, 且决策者可以根据自身风险偏好调节参数κ以最大程度地满足主体意愿。相比ORESTE、TOPSIS等决策方法, 所提的改进ORESTE匹配模型能够更加合理有效地计算排序值来获得最优匹配对。

关键词: 人岗匹配, 概率语言广义兰氏距离, 博弈论, 改进ORESTE排序法, 新匹配意愿系数

Abstract:

With respect to the personnel-position matching in an information environment characterized by a multiattribute probabilistic linguistic set, a two-sided matching model is developed based on an improved ORESTE ranking method and matching aspiration. The proposed model introduces a probabilistic linguistic generalized Lance distance formula, employs a probabilistic linguistic power mean operator to determine the objective attribute weights, and optimizes the combination of subjective and objective weights based on the principles of game theory. This method overcomes the impact of extreme values on decision outcomes and ensures that attribute weights consider both the subjective analysis of decision makers' experiential judgments and the objective analysis of information structures, thereby enhancing scientific validity. Subsequently, the ORESTE ranking method is enhanced by incorporating the probabilistic linguistic generalized Lance distance formula and the Borda function, considering both weak and strong rankings. By simultaneously optimizing the combination of subjective and objective weights, the ranking results became more realistic and aligned with practical scenarios. To maximize satisfaction of the subjects' preferences, a new matching aspiration coefficient, embodying stability and based on the psychological ″anchoring effect, ″ is proposed. This contributes to the construction of a rational and effective multiobjective two-sided matching model. The results of a case study involving personnel-job matching in a smart elderly care service platform demonstrate that the proposed two-sided matching model is effective and that decision-makers can adjust the parameters of κ based on their own risk preferences to maximize satisfaction with the subject's aspiration. Compared with decision methods such as ORESTE and TOPSIS, the proposed improved ORESTE matching model can obtain ranking values to obtain the optimal matching pair more reasonably and effectively.

Key words: personnel-position matching, generalized Lance distance of probabilistic linguistic, game theory, improved ORESTE ranking method, new matching aspiration coefficient